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Automation and Computation (AUTOCOM), International Conference on

作者： Hemant Kumar Yadav Varsha Srivastava Sreenu Banoth Rohini Daksh Rawat Priyansha Rawat Artificial Intelligence & Data Science IIMT College of Engineering Greater Noida Uttar Pradesh India School of Computer Science and Engineering IILM University Greater Noida Uttar Pradesh Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

In this study, an automated system to detect tomato leaf diseases with Convolutional neural networks (CNNs) is introduced to aid farmers in early disease identification to avert crop losses and promote yield. The dataset is multiple classes of tomato leaf diseases, and the model was trained and tuned extensively for its performance. In particular, a high training accuracy of 95.73%, and validation accuracy of 89.85% was achieved by CNN. Furthermore, the system's precision, recall, and F1 metric were around 90%, indicating the balanced system performance. The proposed CNN model is shown to be more reliable and faster than the traditional image processing techniques and hence is an appropriate choice for real time agricultural applications. The integration of modern varieties technologies in AI based approaches in agriculture which will feed productivity while maintaining Sustainability. The results indicate that the proposed system could provide an effective means for disease detection for farmers and may alleviate some of the need for chemical treatments.

关键词： Training Productivity Plant diseases Accuracy Crops Predictive models Agriculture Convolutional neural networks Monitoring Diseases

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Enhancing Industrial Automation Flexibility Through neural Network-Empowered Machine Vision applications 1st

Enhancing Industrial Automation Flexibility Through Neural N...

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1st International Conference on artificial Intelligence and its applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (纸本)9783031843969

Vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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Imagined Speech and Visual imagery as Intuitive Paradigms for Brain-Computer Interfaces

Imagined Speech and Visual Imagery as Intuitive Paradigms fo...

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International Winter Workshop on Brain-Computer Interface (BCI)

作者： Seo-Hyun Lee Ji-Ha Park Deok-Seon Kim Dept. of Brain and Cognitive Engineering Korea University Seoul Republic of Korea Dept. of Artificial Intelligence Korea University Seoul Republic of Korea

ISBN: (数字)9798331521929

ISBN: (纸本)9798331521936

Brain-computer interfaces (BCIs) have shown promise in supporting communication for individuals with motor or speech impairments. Recent advancements such as brain-to-speech or brain-to-image technology aim to reconstruct speech from neural activity. However, robust decoding of communication-related paradigms, such as imagined speech and visual imagery, using non-invasive techniques still remains challenging. This study analyzes brain dynamics in these two paradigms by examining neural synchronization and functional connectivity through phase-locking values (PLV) in noninvasively collected EEG data. Results show that visual imagery produces higher PLV values in visual cortex, engaging spatial networks, while imagined speech demonstrates consistent synchronization, primarily engaging language-related regions. These findings suggest that imagined speech is suitable for language-driven BCI applications, while visual imagery can complement BCI systems for users with speech impairments. Furthermore, the brain connectivity results implies that personalized calibration is crucial for optimizing BCI performance.

关键词： Visualization Communication systems neural activity Signal processing Motors Brain-computer interfaces Decoding Calibration Synchronization Speech processing

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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CNN-Based Plant Leaf Disease Identification and Classification for Sustainable Agriculture

CNN-Based Plant Leaf Disease Identification and Classificati...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： A Srilatha P Praveen School of Computer Science & Artificial Intelligence SR University Warangal India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The rapid spread of leaf-diseases among the large-scale crops or plants will pose a great risk to the agricultural output and crop yield. Earlier detection and proper analysis of foliar disorders are essential for minimizing yield losses as well as forwarding continuous farming trials. This research presents a novel way to find and analyze leaf diseases using various deep learning algorithms. The proposed approach integrates Convolutional neural networks (CNN) in feature extraction for automatically identifying disease patterns from leaf-based images with high accuracy to exploit manual inspection of plant leaves. To achieve higher accuracy, this model is train and tested on a wide variety from diseased & healthy leaf images. This approach surpasses traditional image processing methods, exhibiting high precision and recall in identifying various disease types across multiple crops. The deep learning-based solution offers a scalable, efficient, and reliable tool for farmers and agricultural experts, potentially revolutionizing plant disease management strategies. In addition, it can expand the dataset and enhance real-time detection ability which probably could be applied into user-friendly mobile applications in future work

关键词： Deep learning Plant diseases Accuracy Plants (biology) User interfaces Feature extraction Real-time systems Convolutional neural networks Reliability Diseases

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applications of artificial neural networks in image processing V

Applications of artificial neural networks in image processi...

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2000年

作者： Nasrabadi Nasser M. Katsaggelos Aggelos Konstantinos IS & T--the Society for Imaging Science and Technology. Society of Photo-optical Instrumentation Engineers

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Exploring Parallelism in ZynqNet AI Engine to Accelerate image Classification in Aerospace

Exploring Parallelism in ZynqNet AI Engine to Accelerate Ima...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Eduardo Marañon Aguilar Fabio Benevenuti Fernanda Lima Kastensmidt Federal University of Rio Grande do Sul (UFRGS) Institute of Informatics Microelectronics Graduate Program (PGMICRO) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This research investigates various configurations of the Zynqnet AI Engine to accelerate image classification tasks using the SAT-6 dataset and a 4-layer CNN. The C++ code for the hardware accelerator was translated into RTL using Vivado HLS and integrated with the Zynq processing System on a ZedBoard Zynq-7000 ARM/FPGA SoC. The AI engine was configured to work with 32 -bit floating point, 8bit fixed point and 8 -bit integer type operations, with different processing elements for parallelizing CNN tasks. Given a 64-bit data memory bus in communication with the accelerator and an 8-bit integer quantization, the variation in the number of processing elements does not result in a significant performance improvement. To address this, 8 values were grouped for reading and writing in 64-bit parallel mode, and different communication architectures were designed to improve data transfer between the accelerator and DDR memory, with optional enablement of L1D and L2 cache. The research analyzes CNN computation performance and FPGA resource usage (LUTs, FFs, BRAMs, DSPs) across configurations, providing insights into the trade-offs in FPGA-based CNN implementations, aiding the development of efficient, high-performance hardware accelerators for Deep Learning applications.

关键词： Quantization (signal) Writing Table lookup System-on-chip Convolutional neural networks artificial intelligence Hardware acceleration Engines Field programmable gate arrays image classification

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Intelligent Human Behaviour Detection Leveraging ConvLSTM and LRCN Deep Learning Approaches

Intelligent Human Behaviour Detection Leveraging ConvLSTM an...

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International Conference on Signal processing and Communication (ICSC)

作者： S Chakradhar Amingad Harika Janmanchi Mohammed Sultan K Shalini S Hariharan Bhanu Prakash Maddala Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Artificial Intelligence and Data Science Vardhaman college of engineering Hyderabad India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This research delves into intelligent human behavior detection from video sequences by employing advanced deep learning methodologies. The study harnesses the capabilities of Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional networks (LRCN) architectures, which are adept at capturing both temporal and spatial dynamics in video data. Using the extensive Kinetics dataset, which includes a wide range of human activities and contexts, the video data was converted into sequential image frames for model training. The ConvLSTM and LRCN models were implemented and trained with the TensorFlow/Keras framework on GPU-accelerated systems. During training, both models exhibited effective learning, with improved accuracy and reduced loss. However, validation metrics revealed fluctuations, indicating possible overfitting and challenges in generalizing to new data. To address these issues, regularization techniques such as dropout and early stopping were applied, alongside comprehensive hyperparameter tuning. Additionally, the study investigated the use of transfer learning to enhance model performance. This research lays the groundwork for sophisticated video-based human behavior detection systems, with applications in surveillance, healthcare, and human-computer interaction. Future work will aim to bolster model robustness and broaden the scope of action recognition.

关键词： Training Deep learning Surveillance Video sequences Transfer learning Robustness Behavioral sciences Convolutional neural networks Long short term memory Tuning

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Deepfake image and Video Detection Using Deep Learning Algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks artificial intelligence Research and development

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Advancements in automated nuclei segmentation for histopathology using you only look once-driven approaches: A systematic review

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Computers in Biology and Medicine 2025年 190卷 110072-110072页

作者： Debsarkar, Shyam Sundar Aronow, Bruce Prasath, V.B. Surya Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center OH45229 United States Department of Pediatrics College of Medicine University of Cincinnati OH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States Department of Computer Science University of Cincinnati OH45221 United States

Histopathology image analysis plays a pivotal role in disease diagnosis and treatment planning, relying heavily on accurate nuclei segmentation for extracting vital cellular information. In recent years, artificial intelligence (AI) and in particular deep learning models have been applied successfully in solving computational pathology image analysis tasks. The You Only Look Once (YOLO) object detection framework, which is based on a convolutional neural network (CNN) architecture has gained traction across various domains for its real-time processing capabilities. This systematic review aims to comprehensively explore and evaluate the advancements, challenges, and applications of YOLO-based methodologies in nuclei segmentation within the domain of histopathological images. The review encompasses a structured analysis of recent literature, focusing on the utilization of YOLO variants for nuclei segmentation. Key methodologies, training strategies, dataset specifics, and performance metrics are evaluated to elucidate the strengths and limitations of YOLO in this context. Additionally, the review highlights the unique characteristics of YOLO that enable efficient object detection and delineation of nuclei structures, offering a comparative analysis against traditional segmentation approaches. This systematic review underscores the promising outcomes achieved through YOLO-based architectures, emphasizing their potential for accurate and rapid nuclei segmentation. Furthermore, it identifies persistent challenges such as handling variances in nuclei appearances, optimizing model architectures for histopathological images, and improving generalization across diverse datasets. Insights derived from this review can provide a foundation for future research directions and enhancements in nuclei segmentation methodologies using YOLO within histopathology, fostering advancements in disease diagnosis and biomedical research. © 2025 The Authors

关键词： Convolutional neural networks

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